In this section, we present the reconstructions from our approach and compare them with the existing state-of-the-art approaches. To being with, we illustrate the ability of an autoregressive prior in reconstructing pixel level details using an example of missing pixel inpainting in an image. For this, we randomly mask out pixels from the image and use our prior to reconstruct these missing pixels. We perform by keeping the observed pixel values as same and update missing pixels to maximize the the prior loglikelihood. Specifically, we take an image of size 384x512 and mask 80% of the pixels in the initial image as could be seen in Figure REF . We compare our results with that of OneNet [1]}, and we can observe details in our reconstruction much better like the text outlines, also quantitatively in terms of PSNR and SSIM. We use a step size of 75 and run for approximately 1000 iterations.
